Update app.py

app.py

@@ -3,7 +3,6 @@ import requests
 import json
 from decouple import Config
 
-# Function to interact with Vectara API
 def query_vectara(question, chat_history, uploaded_file):
     # Handle file upload to Vectara
     customer_id = config('CUSTOMER_ID')  # Read from .env file
@@ -20,7 +19,7 @@ def query_vectara(question, chat_history, uploaded_file):
         "file": (uploaded_file.name, uploaded_file),
         "doc_metadata": (None, json.dumps({"metadata_key": "metadata_value"})),  # Replace with your metadata
     }
-    response = requests.post(url, files=files, verify=True, headers=post_headers)
+    response = requests.post(url, files=files, headers=post_headers)
 
     if response.status_code == 200:
         upload_status = "File uploaded successfully"
@@ -30,48 +29,68 @@ def query_vectara(question, chat_history, uploaded_file):
     # Get the user's message from the chat history
     user_message = chat_history[-1][0]
 
+    # Query Vectara API
+    query_url = "https://api.vectara.io/v1/query/v1/query"
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}",
+        "customer-id": customer_id,
+    }
+
     query_body = {
         "query": [
             {
-                "query": user_message,  # Use the user's message as the query
+                "query": user_message,
+                "queryContext": "",
                 "start": 0,
                 "numResults": 10,
+                "contextConfig": {
+                    "charsBefore": 0,
+                    "charsAfter": 0,
+                    "sentencesBefore": 2,
+                    "sentencesAfter": 2,
+                    "startTag": "%START_SNIPPET%",
+                    "endTag": "%END_SNIPPET%",
+                },
+                "rerankingConfig": {
+                    "rerankerId": 272725718,
+                    "mmrConfig": {
+                        "diversityBias": 0.3
+                    }
+                },
                 "corpusKey": [
                     {
                         "customerId": customer_id,
                         "corpusId": corpus_id,
-                        "lexicalInterpolationConfig": {"lambda": 0.025}
+                        "semantics": 0,
+                        "metadataFilter": "",
+                        "lexicalInterpolationConfig": {
+                            "lambda": 0
+                        },
+                        "dim": []
+                    }
+                ],
+                "summary": [
+                    {
+                        "maxSummarizedResults": 5,
+                        "responseLang": "eng",
+                        "summarizerPromptName": "vectara-summary-ext-v1.2.0"
                     }
                 ]
             }
         ]
     }
 
-    api_endpoint = "https://api.vectara.io/v1/query"
-    return f"{upload_status}\n\nResponse from Vectara API: {response.text}"
+    query_response = requests.post(query_url, json=query_body, headers=headers)
 
-# from vectara_retriever import VectaraRetriever  # Assuming VectaraRetriever is in vectara_retriever.py
+    if query_response.status_code == 200:
+        query_data = query_response.json()
+        response_message = f"{upload_status}\n\nResponse from Vectara API: {json.dumps(query_data, indent=2)}"
+    else:
+        response_message = f"{upload_status}\n\nError: {query_response.status_code}"
 
-# Function to interact with Vectara API
-# def query_vectara(question, chat_history, uploaded_file):
-#    # Handle file upload to Vectara
-#
-#    # Get the user's message from the chat history
-#    user_message = chat_history[-1][0]
-#
-#    # Create a VectaraRetriever instance
-#    retriever = VectaraRetriever(index=your_vectara_index)  # Replace with your VectaraIndex instance
-#
-#    # Create a QueryBundle with the user's message
-#    query_bundle = QueryBundle(query_str=user_message)
-#
-#    # Retrieve the top k most similar nodes
-#    top_k_nodes = retriever._retrieve(query_bundle)
-#
-#    # Format the nodes for display
-#    responses = "\n".join([f"{node.node.text} (score: {node.score})" for node in top_k_nodes])
-#
-#    return f"{upload_status}\n\nResponse from Vectara API: {responses}"
+    return response_message
     
 # Create a Gradio ChatInterface with a text input, a file upload input, and a text output
 iface = gr.Interface(
@@ -82,4 +101,5 @@ iface = gr.Interface(
     description="Ask me anything using the Vectara API!"
 )
 
-iface.launch()
+iface.launch()
+